Biology subjects

Wareham, N. J.

Publications and source records attributed to Wareham, N. J..

10 recordsLinked to original sources

bioRxiv2024

Systemic inflammation is a minor contributor to remnant cholesterol atherogenicity

BackgroundBoth plasma levels of remnant cholesterol and low-density lipoprotein cholesterol (LDL-C) levels are independent risk factors for atherosclerotic cardiovascular disease. However, only remnant cholesterol has consistently been associated with systemic inflammation. The extent to which inflammation mediates the effect of remnant cholesterol on major adverse cardiovascular events (MACE) remains unclear. Methods and ResultsThis study included 16,445 participants without prior atherosclerotic cardiovascular disease from the EPIC-Norfolk cohort, with a mean age of 58.8{+/-}9.1 years, of which 9,357 (56.9%) were women. Every 1 mmol/L higher remnant cholesterol was associated with 29.5% higher hsCRP levels (95% Confidence Interval (CI): 22.1, 37.4, p<0.001), whereas LDL-C was not significantly associated with hsCRP levels in the fully adjusted model. Additionally, each 1 mmol/L higher remnant cholesterol was associated with a hazard ratio (HR) of 1.31 (95% CI: 1.14, 1.50, p<0.001) for MACE, compared to a HR of 1.21 (95% CI: 1.13, 1.31, p<0.001) for LDL-C. Mediation analysis showed that hsCRP mediated 5.9% (95% CI: 1.2, 10.6%, p<0.001) of the effect of remnant cholesterol on MACE, whereas hsCRP did not mediate the effect of LDL-C. ConclusionsPlasma remnant cholesterol levels are independently associated with systemic inflammation and cardiovascular events. Inflammation, as measured with hsCRP, contributed minorly to the association between remnant cholesterol and MACE. This underscores the need to address both remnant cholesterol and systemic inflammation separately in the clinical management of cardiovascular disease. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=186 SRC="FIGDIR/small/604203v1_ufig1.gif" ALT="Figure 1"> View larger version (54K): org.highwire.dtl.DTLVardef@bdbb4org.highwire.dtl.DTLVardef@850ea6org.highwire.dtl.DTLVardef@184f321org.highwire.dtl.DTLVardef@17616fa_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstract:C_FLOATNO The study assessed the relationship between remnant cholesterol, systemic inflammation, and MACE risk in 16,445 participants free from atherosclerotic cardiovascular disease from the EPIC-Norfolk cohort. Every 1 mmol/L higher remnant cholesterol was associated with 29.5% higher hsCRP levels (95% CI: 22.1, 37.4, p<0.001), while LDL cholesterol was not significantly associated with hsCRP levels. Additionally, each 1 mmol/L higher remnant cholesterol was associated with a HR of 1.31 (95% CI: 1.14, 1.50, p<0.001) for MACE, compared to a HR of 1.21 (95% CI: 1.13, 1.31, p<0.001) for LDL-C. hsCRP mediated 5.9% (95% CI: 1.2, 10.6%, p<0.001) of the effect of remnant cholesterol on MACE, while it did not mediate the effect of LDL cholesterol. LDL: low-density lipoprotein cholesterol, HR: hazard ratio, CI: confidence interval, MACE: major adverse cardiovascular events. C_FIG

systems biology↗

bioRxiv2021

Cross-platform proteomics to advance genetic prioritisation strategies

Discovery of protein quantitative trait loci (pQTLs) has been enabled by affinity-based proteomic techniques and is increasingly used to guide genetically informed drug target evaluation. Large-scale proteomic data are now being created, but systematic, bidirectional assessment of platform differences is lacking, restricting clinical translation. We compared genetic, technical, and phenotypic determinants of 871 protein targets measured using both aptamer-(SomaScan(R) Platform v4) and antibody-based (Olink) assays in up to 10,708 individuals. Correlations coefficients for overlapping protein targets varied widely (median 0.38, IQR: 0.08-0.64). We found that 64% of pQTLs were shared across both platforms among all identified 608 cis- and 1,315 trans-pQTLs with sufficient power for replication, but with correlations of effect estimates being lower than previously reported (cis: 0.41, trans: 0.34). We identified technical, protein, and variant characteristics that contributed significantly to platform differences and found contradicting phenotypic associations attributable to those. We demonstrate how integrating phenomic and gene expression data improves genetic prioritisation strategies, including platform-specific pQTLs.

genetics↗

bioRxiv2020

Genetic architecture of host proteins interacting with SARS-CoV-2

Strategies to develop therapeutics for SARS-CoV-2 infection may be informed by experimental identification of viral-host protein interactions in cellular assays and measurement of host response proteins in COVID-19 patients. Identification of genetic variants that influence the level or activity of these proteins in the host could enable rapid in silico assessment in human genetic studies of their causal relevance as molecular targets for new or repurposed drugs to treat COVID-19. We integrated large-scale genomic and aptamer-based plasma proteomic data from 10,708 individuals to characterize the genetic architecture of 179 host proteins reported to interact with SARS-CoV-2 proteins or to participate in the host response to COVID-19. We identified 220 host DNA sequence variants acting in cis (MAF 0.01-49.9%) and explaining 0.3-70.9% of the variance of 97 of these proteins, including 45 with no previously known protein quantitative trait loci (pQTL) and 38 encoding current drug targets. Systematic characterization of pQTLs across the phenome identified protein-drug-disease links, evidence that putative viral interaction partners such as MARK3 affect immune response, and establish the first link between a recently reported variant for respiratory failure of COVID-19 patients at the ABO locus and hypercoagulation, i.e. maladaptive host response. Our results accelerate the evaluation and prioritization of new drug development programmes and repurposing of trials to prevent, treat or reduce adverse outcomes. Rapid sharing and dynamic and detailed interrogation of results is facilitated through an interactive webserver (https://omicscience.org/apps/covidpgwas/).

genomics↗

bioRxiv2020

Cross-platform genetic discovery of small molecule products of metabolism and application to clinical outcomes

Circulating levels of small molecules or metabolites are highly heritable, but the impact of genetic differences in metabolism on human health is not well understood. In this cross-platform, genome-wide meta-analysis of 174 metabolite levels across six cohorts including up to 86,507 participants (70% unpublished data), we identify 499 (362 novel) genome-wide significant associations (p<4.9×10-10) at 144 (94 novel) genomic regions. We show that inheritance of blood metabolite levels in the general population is characterized by pleiotropy, allelic heterogeneity, rare and common variants with large effects, non-linear associations, and enrichment for nonsynonymous variation in transporter and enzyme encoding genes. The majority of identified genes are known to be involved in biochemical processes regulating metabolite levels and to cause monogenic inborn errors of metabolism linked to specific metabolites, such as ASNS (rs17345286, MAF=0.27) and asparagine levels. We illustrate the influence of metabolite-associated variants on human health including a shared signal at GLP2R (p.Asp470Asn) associated with higher citrulline levels, body mass index, fasting glucose-dependent insulinotropic peptide and type 2 diabetes risk, and demonstrate beta-arrestin signalling as the underlying mechanism in cellular models. We link genetically-higher serine levels to a 95% reduction in the likelihood of developing macular telangiectasia type 2 [odds ratio (95% confidence interval) per standard deviation higher levels 0.05 (0.03-0.08; p=9.5×10-30)]. We further demonstrate the predictive value of genetic variants identified for serine or glycine levels for this rare and difficult to diagnose degenerative retinal disease [area under the receiver operating characteristic curve: 0.73 (95% confidence interval: 0.70-0.75)], for which low serine availability, through generation of deoxysphingolipids, has recently been shown to be causally relevant. These results show that integration of human genomic variation with circulating small molecule data obtained across different measurement platforms enables efficient discovery of genetic regulators of human metabolism and translation into clinical insights.Competing Interest StatementA.S.B. has received grants from AstraZeneca, Biogen, Bioverativ, Merck, Novartis, and Sanofi. J. D. sits on the International Cardiovascular and Metabolic Advisory Board for Novartis (since 2010), the Steering Committee of UK Biobank (since 2011), the MRC International Advisory Group (ING) member, London (since 2013), the MRC High Throughput Science Omics Panel Member, London (since 2013), the Scientific Advisory Committee for Sanofi (since 2013), the International Cardiovascular and Metabolism Research and Development Portfolio Committee for Novartis and the Astra Zeneca Genomics Advisory Board (2018). E.B.F. is an employee and stock holder of Pfizer.View Full Text

genetics↗

bioRxiv2019

Genetic inhibition of PCSK9, atherogenic lipoprotein concentrations, and calcific aortic valve stenosis

BackgroundProprotein convertase subtilisin/kexin type 9 (PCSK9) inhibition reduces plasma concentrations of low-density lipoprotein cholesterol (LDL-C), apolipoprotein B (apoB) and lipoprotein(a) [Lp(a)]. Atherogenic lipoprotein levels have been linked with calcific aortic valve stenosis (CAVS). Our objectives were to determine the association between variants in PCSK9 and lipoprotein-lipid levels, coronary artery disease (CAD) and CAVS, and to evaluate if PCSK9 could be implicated in aortic valve interstitial cells (VICs) calcification. MethodsWe built a genetic risk score weight for LDL-C levels (wGRS) using 10 independent PCSK9 single nucleotide polymorphisms and determined its association with lipoprotein-lipid levels in 9692 participants of the EPIC-Norfolk study. We investigated the association between the wGRS and CAD and CAVS in the UK Biobank, as well as the association between the PCSK9 R46L variant and CAVS in a meta-analysis of published prospective, population-based studies (Copenhagen studies, 1463 cases/101,620 controls) and unpublished studies (UK Biobank, 1350 cases/349,043 controls, Malmo Diet and Cancer study, 682 cases/5963 controls and EPIC-Norfolk study, 508 cases/20,421 controls). We evaluated PCSK9 expression and localization in explanted aortic valves by capillary Western blot and immunohistochemistry in patients with and without CAVS. Von Kossa staining was used to visualize aortic leaflet calcium deposits. PCSK9 expression under oxidative stress conditions in VICs was assessed. ResultsThe wGRS was significantly associated with lower LDL-C and apoB (p<0.001), but not with Lp(a). In the UK Biobank, the association of PCSK9 variants with CAD were positively correlated with their effects on apoB levels. CAVS was less prevalent in carriers of the PCSK9 R46L variant [odds ratio=0.71 (95% confidence interval, 0.57-0.88), p<0.001]. PCSK9 expression was elevated in the aortic valves of patients with aortic sclerosis and CAVS compared to controls. In calcified leaflets, PCSK9 co-localized with calcium deposits. PCSK9 expression was induced by oxidative stress in VICs. ConclusionGenetic inhibition of PCSK9 is associated with lifelong reductions in the levels of non-Lp(a) apoB-containing lipoproteins as well as lower odds of CAD and CAVS. PCSK9 is abundant in fibrotic and calcified aortic leaflets. Oxidative stress increases PCSK9 expression in VICs. These results provide a rationale for performing randomized clinical trials of PCSK9 inhibition in CAVS.

genetics↗

bioRxiv2019

Regional fat depot masses are influenced by protein-coding gene variants

With the identification of a large number of genetic loci associated with human fat distribution and its importance for metabolic health, the question arises as to what the genetic drivers for discrete fat depot expansion might be. To date most studies have focussed on conventional anthropometric measures such as waist-to-hip ratio (WHR) adjusted for body mass index. We searched for genetic loci determining discrete fat depots mass size using an exome-wide approach in 3 large cohorts. Here we report an exome-wide analysis of non-synonymous genetic variants in 17,212 participants in which regional fat masses were quantified using dual-energy X-ray absorptiometry. The missense variant CCDC92S70C, previously associated with WHR, is associated specifically with reduced visceral and increased leg fat masses. Allele-specific expression analysis shows that the deleterious minor allele carrying transcript also has a constitutively higher expression. In addition, we identify two variants associated with the transcriptionally distinct fat depot arm fat (SPATA20K422R and UQCC1R51Q). SPATA20K422R, a rare novel locus with a large effect size specific to arm, and UQCC1R51Q, a common variant exome-wide significant in arm but showing similar trends in other subcutaneous fat depots. In terms of the understanding of human fat distribution, these findings suggest distinct regulation of discrete fat depot expansion. Author summaryHuman fat storing tissues are heterogeneous and comprise functionally and structurally distinct regional fat depots, the relative size of which appear to have significant implications for health. Whilst it is known that inter-individual differences in fat distribution have genetic drivers, studies to date have focussed on crude anthropometric approximations of region fat masses rather than precise measures. Here we describe an exome-wide analysis of a large collection of men and women who have undergone body scanning using dual-energy X-ray absorptiometry (DXA) to better define regional fat masses and identify new genetic drivers for human fat distribution. With this approach we identify three gene regions associated with distinct fat depots which can help to explain the variation in fat distribution between people and may lead to a better understanding of the depot specific fat tissue expansion.

genomics↗

bioRxiv2019

Harmonisation of Physical Activity Variables by Indirect Validation: A Doubly Labelled Water Study

Harmonisation of data for pooled analysis relies on the principle of inferential equivalence between variables from different sources. Ideally, this is achieved using models of the direct relationship with gold standard criterion measures, but the necessary validation data are often unavailable. This study examines an alternative method of harmonisation by indirect validation. Starting methods were self-report or accelerometry, from which we derived indirect models of relationships with doubly labelled water (DLW)-measured physical activity energy expenditure (PAEE) using sets of two bridge equations via one of three intermediate measures. Coefficients and performance of indirect models were compared to corresponding direct validation models (linear regression of DLW-measured PAEE on starting methods). Indirect model beta coefficients were attenuated compared to direct model betas (10-63%), narrowing the range of PAEE values; attenuation was greater when bridge equations were weak. Directly and indirectly harmonised models had similar error variance but most indirectly derived values were biased at group-level. Correlations with DLW-measured PAEE were identical after harmonisation using continuous linear but not categorical models. Wrist acceleration harmonised to DLW-measured PAEE via combined accelerometry and heart rate sensing had lowest error variance (24.5%) and non-significant mean bias 0.9 (95%CI: -1.6; 3.4) kJ*day-1*kg-1. Associations between PAEE and BMI were similar for directly and indirectly harmonised values, but most fell outside the confidence interval of the criterion PAEE-to-BMI association. Indirect models can be used for harmonisation. Performance depends on the measurement properties of original data, variance explained by available bridge equations, and similarity of population characteristics.

epidemiology↗

bioRxiv2018

Genomic analysis of diet composition finds novel loci and associations with health and lifestyle

We conducted genome-wide association study (GWAS) meta-analyses of relative caloric intake from fat, protein, carbohydrates and sugar in over 235,000 individuals. We identified 21 approximately independent lead SNPs. Relative protein intake exhibits the strongest relationships with poor health, including positive genetic associations with obesity, type 2 diabetes, and heart disease (rg {approx} 0.15 - 0.5). Relative carbohydrate and sugar intake have negative genetic correlations with waist circumference, waist-hip ratio, and neighborhood poverty (|rg| {approx} 0.1 - 0.3). Overall, our results show that the relative intake of each macronutrient has a distinct genetic architecture and pattern of genetic correlations suggestive of health implications beyond caloric content.

genetics↗

bioRxiv2018

Genome-wide scan and fine-mapping of rare nonsynonymous associations implicates intracellular lipolysis genes in fat distribution and cardio-metabolic risk

Difficulties in identifying causal variants and genes underlying genetic associations have limited the translational potential of genetic studies of body fat distribution, an important, partly-heritable risk factor for cardio-metabolic disease. Rare variant associations facilitate fine-mapping of causal alleles, but their contribution to fat distribution is understudied. We performed a genome-wide scan of rare nonsynonymous variants for body mass index-adjusted waist-to-hip-ratio (BMI-adjusted WHR; a widely-used measure of fat distribution) in 450,562 European ancestry individuals, followed by systematic Bayesian fine-mapping at six genome-wide (p<5x10-08; main-analysis) and two subthreshold signals (significant at a Bonferroni-corrected p<1.3x10-06). We found strong statistical evidence of causal association for nonsynonymous alleles in CALCRL (p.L87P, pconditional=5.9x10-12; posterior-probability of association [PPA]=52%), PLIN1 (p.L90P, pconditional=5.5x10-13; PPA>99%), PDE3B (p.R783X, pconditional=6.2x10-15; PPA>99%), ACVR1C (p.I195T; pconditional=5.4x10-12; PPA>99%), and FGF1 (p.G21E, pconditional=1.6x10-07; PPA=98%). Alleles at the four likely-causal main-analysis genes affected fat distribution primarily via larger hip-rather than smaller waist-circumference and six of nine conditionally-independent WHR-lowering index-variants were associated with protection from cardiovascular or metabolic disease. All four genes are expressed in adipose tissue and have been linked with the regulation of intracellular lipolysis, which controls fat retention in mature cells. Targeted follow-up analyses of key intracellular-lipolysis genes revealed associations for a variant in the initiator of intracellular lipolysis PNPLA2 (p.N252K) with higher BMI-adjusted-WHR and higher cardio-metabolic risk. This study provides human genetic evidence of a link between intracellular lipolysis, fat-distribution and its cardio-metabolic complications in the general population.

genetics↗

bioRxiv2017

A Comprehensive Reanalysis Of Publicly Available GWAS Datasets Reveals An X Chromosome Rare Regulatory Variant Associated With High Risk For Type 2 Diabetes.

The reanalysis of publicly available GWAS data represents a powerful and cost-effective opportunity to gain insights into the genetics and pathophysiology of complex diseases. We demonstrate this by gathering and reanalyzing public type 2 diabetes (T2D) GWAS data for 70,127 subjects, using an innovative imputation and association strategy based on multiple reference panels (1000G and UK10K). This approach led us replicate and fine map 50 known T2D loci, and identify seven novel associated regions: five driven by common variants in or near LYPLAL1, NEUROG3, CAMKK2, ABO and GIP genes; one by a low frequency variant near EHMT2; and one driven by a rare variant in chromosome Xq23, associated with a 2.7-fold increased risk for T2D in males, and located within an active enhancer associated with the expression of Angiotensin II Receptor type 2 gene (AGTR2), a known modulator of insulin sensitivity. We further show that the risk T allele reduces binding of a nuclear protein, resulting in increased enhancer activity in muscle cells. Beyond providing novel insights into the genetics and pathophysiology of T2D, these results also underscore the value of reanalyzing publicly available data using novel analytical approaches.

genetics↗